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. 2023 Mar 30:31:100365.
doi: 10.1016/j.jctube.2023.100365. eCollection 2023 May.

Artificial intelligence-reported chest X-ray findings of culture-confirmed pulmonary tuberculosis in people with and without diabetes

Coralie Geric  1   2 , Arman Majidulla  3 , Gamuchirai Tavaziva  1   2 , Ahsana Nazish  4 , Saima Saeed  4 , Andrea Benedetti  1   2   5 , Aamir J Khan  6 , Faiz Ahmad Khan  1   2   5
Affiliations

Artificial intelligence-reported chest X-ray findings of culture-confirmed pulmonary tuberculosis in people with and without diabetes

Coralie Geric et al. J Clin Tuberc Other Mycobact Dis. .

Abstract

Objectives: We applied computer-aided detection (CAD) software for chest X-ray (CXR) analysis to determine if diabetes affects the radiographic presentation of tuberculosis.

Methods: From March 2017-July 2018, we consecutively enrolled adults being evaluated for pulmonary tuberculosis in Karachi, Pakistan. Participants had same-day CXR, two sputum mycobacterial cultures, and random blood glucose measurement. We identified diabetes through self-report or glucose >11.1mMol/L. We included participants with culture-confirmed tuberculosis for this analysis. We used linear regression to estimate associations between CAD-reported tuberculosis abnormality score (range 0.00 to 1.00) and diabetes, adjusting for age, body mass index, sputum smear-status, and prior tuberculosis. We also compared radiographic abnormalities between participants with and without diabetes.

Results: 63/272 (23%) of included participants had diabetes. After adjustment, diabetes was associated with higher CAD tuberculosis abnormality scores (p < 0.001). Diabetes was not associated with frequency of CAD-reported radiographic abnormalities apart from cavitary disease; participants with diabetes were more likely to have cavitary disease (74.6% vs 61.2% p = 0.07), particularly non-upper zone cavitary disease (17% vs 7.8%, p = 0.09).

Conclusions: CAD analysis of CXR suggests diabetes is associated with more extensive radiographic abnormalities and with greater likelihood of cavities outside upper lung zones.

Keywords: Chest X-ray; Deep learning; Diabetes; Tuberculosis.

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Conflict of interest statement

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: CG, AM, GT, AN, SS, and AB have no potential conflicts of interest to disclose. Aamir J. Khan has had financial interests in the company Alcela, of which qure.ai is a client, and has provided technical assistance to quire.ai data scientists on the development of an all-in-artificial intelligence algorithm for mass CXR screening in public health programs. Consulting started in Q4 2019 and ended in Q2 2020. Solution was not finalized, and the planned product was not made commercially available. Aamir J. Khan had helped conceive and design the included study from Pakistan in 2016 to 2017, but was never directly involved in data collection, analysis or reporting of that study and his relationship with Alcela arose after the completion of data collection for that study. Aamir J. Khan was not involved in the design, analysis, reporting, writing, editing, or decision to submit the work reported in the present manuscript. As Principal Investigator, Faiz Ahmad Khan received the grant from the Canadian Institutes of Health Research (CIHR) to undertake the original study of CAD diagnostic accuracy in Pakistan whose data are analyzed for the present work. The present work is partially funded by a peer-reviewed grant from l'Observatoire International sur les Impacts Sociétaux de l'IA et du Numérique, which is a publicly funded observatory of the Fonds de Recherche du Quebec, which itself is the provincial research funding agency in the province of Quebec. FAK currently holds a grant from CIHR to study CAD in Canada. FAK also reports salary support from the Fonds de Recherche du Quebec Santé. Qure.ai (India) the developer and owner of qXR, the software evaluated in this study, provide our lab with free access to their software. They have no access to data, and no role in collection, analysis and interpretation of data; nor in the writing of reports; nor in the decision to submit work for publication. Delft (Netherlands) the developer and owner of CAD4TB, provide ou rlab with reduced research pricing access to their software. They have no access to data, and no role in collection, analysis and interpretation of data; nor in the writing of reports; nor in the decision to submit work for publication.

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